Energy efficient clustering algorithm in wireless sensor networks using fuzzy logic control

Siew, Zhan Wei; Kiring, Aroland; Yew, Hoe Tung; Neelakantan, P.; Teo, Kenneth Tze Kin

doi:10.1109/chuser.2011.6163758

Cited by 27 publications

(13 citation statements)

References 9 publications

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“…Furthermore, by having a random choice, there is a possibility that none or all nodes are elected. Siew et al (2011) proposed a system based on fuzzy logic for choosing CHs. The input variables of the system are the residual energy and the RSSI to sink.…”

Section: Related Workmentioning

confidence: 99%

A distributed algorithm for semantic collectors election in wireless sensors networks

Hermeto

Kridi

Rocha

et al. 2014

jacr

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Abs tract. Semantic clustering is a recent technique for saving energy in wireless sensor networks. Its mechanism of action consists in dividing the network into groups (clusters) formed by semantically related nodes and at least one semantic collector, which acts as a bridge between its internal nodes and the sink node. Since semantic collector nodes need to perform more tasks than normal nodes, they deplete their energy budget faster, so it is necessary to use efficient mechanisms for electing semantic collectors to prolong the network lifetime. Our hypothesis is that an effective choice of semantic collectors allows a longer network lifetime. To test it, we start from a previous work of the authors of this article and we propose an algorithm for electing semantic collectors in a distributed way based on a fuzzy inference engine. The inputs of the inference engine are the residual energy of nodes and their received signal strength indicator (RSSI). Simulation results confirm our hypothesis, since the algorithm provides (i) an improvement of 17.4% in relation to another proposal of the related literature, and (ii) a gain of 68.8% over the time life of the network's original work.

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Section: Related Workmentioning

confidence: 99%

A distributed algorithm for semantic collectors election in wireless sensors networks

Hermeto

Kridi

Rocha

et al. 2014

jacr

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“…In [13] authors proposed a clustering approach which is similar to Gupta approach with two fuzzy input variables. Authors in [14] presented type-2 Takagi-Sugeno-Kang fuzzy logic system in clustering algorithm (ICT2TSK).…”

Section: Related Workmentioning

confidence: 99%

“…In [16] authors investigated and highlighted the limitations of LEACH [1], LEACH-C [10] and CHEF [13] techniques and further presented fuzzy logic based clustering technique LEACH-ERE. The output chance value is evaluated using two fuzzy input variables: expected residual energy (ERE) and residual energy of sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Uniform Segregation of Densely Deployed Wireless Sensor Networks

Singh¹,

Soni²

2017

ijacsa

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Abstract-In wireless sensor networks, the selection of cluster heads relies upon the various selection parameters, such as energy, distance, node concentration and rate of retransmission. There is always uncertainty in the suitability of sensor node for the cluster head role due to these various selection parameters. Fuzzy logic is capable of overcoming uncertainties even with incomplete available information. This quality of fuzzy logic can reduce uncertainty in cluster head selection up to large extent. Therefore, in this paper, a fuzzy logic based clustering approach is proposed to enhance the network operational lifetime. The cluster formation is done on the basis of the spatial correlation value between sensors to organize clusters uniformly in the network. The results are compared with well-known approaches CHEF and LEACH.

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“…After the formation of chain, as shown in Figure 2, the leader selection phase starts, leader is selected with the help of Fuzzy Inference System. For this Mamdani system is used that contains three parts: fuzzifier, inference engine and defuzzifier [13].…”

Section: Proposed Workmentioning

confidence: 99%

Prolonging the Network Lifetime using Fuzzy logic in Wireless Sensor Network

Sharma¹,

Goyal²

2016

IJCA

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Wireless sensor networks include deployment of large number of sensor nodes to sense, process and communicate the data to the sink. The small sized sensor nodes are cheap and can be easily deployed in harsh physical environments but the low battery source for the nodes act as a major limitation for the sensor networks to operate efficiently. The protocols have been proposed in recent times to make optimum use of the energy. Power Efficient Gathering in Sensor Information System (PEGASIS) has been considered as an efficient approach as compared to the Low Energy Adaptive Clustering Hierarchy (LEACH). In this paper, a new approach for selecting the leader of the chain has been proposed using Fuzzy Logic in which residual energy and proximity to the base station has been taken as two parameters and combining these two parameters the leader is selected which aggregates and communicates the data further to the sink. The simulation results have shown that this proposed work has improved the performance metrics of the network and thus extended the lifetime of network.

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Energy efficient clustering algorithm in wireless sensor networks using fuzzy logic control

Cited by 27 publications

References 9 publications

A distributed algorithm for semantic collectors election in wireless sensors networks

A distributed algorithm for semantic collectors election in wireless sensors networks

Uniform Segregation of Densely Deployed Wireless Sensor Networks

Prolonging the Network Lifetime using Fuzzy logic in Wireless Sensor Network

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